SOTAVerified

Domain Generalization

The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain

Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning

Papers

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Showing 1176–1200 of 1751 papers

TitleStatusHype
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference—0
Domain Generalization using Ensemble Learning—0
Domain Generalization using Pretrained Models without Fine-tuning—0
Domain Generalization via Balancing Training Difficulty and Model Capability—0
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis—0
Domain Generalization via Conditional Invariant Representation—0
Domain Generalization via Contrastive Causal Learning—0
Domain Generalization via Domain-based Covariance Minimization—0
Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection—0
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction—0
Domain Generalization via Inference-time Label-Preserving Target Projections—0
Domain Generalization via Invariant Representation under Domain-Class Dependency—0
Domain Generalization via Multidomain Discriminant Analysis—0
Domain Generalization via Selective Consistency Regularization for Time Series Classification—0
Domain Generalization With Adversarial Feature Learning—0
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation—0
Domain Generalization with Domain-Specific Aggregation Modules—0
Domain Generalization with MixStyle—0
Domain Generalization via Optimal Transport with Metric Similarity Learning—0
Domain Generalization without Excess Empirical Risk—0
Domain Generalization with Pseudo-Domain Label for Face Anti-Spoofing—0
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification—0
Domain Generalization with Small Data—0
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning—0
Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer—0
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99—Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6—Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9—Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6—Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4—Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3—Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2—Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97—Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450—Unverified
2VOLO-D5Accuracy - All Images57.2—Unverified
3ConvNeXt-BAccuracy - All Images53.5—Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7—Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5—Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7—Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6—Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1—Unverified
9ViT-16/L-224Accuracy - All Images49—Unverified
10ResNet-50 (gn)Accuracy - All Images48.9—Unverified